Overview

Dataset statistics

Number of variables17
Number of observations466
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.0 KiB
Average record size in memory136.3 B

Variable types

Numeric16
Categorical1

Alerts

feature0 is highly correlated with feature1 and 7 other fieldsHigh correlation
feature1 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature2 is highly correlated with feature0 and 11 other fieldsHigh correlation
feature4 is highly correlated with feature0 and 9 other fieldsHigh correlation
feature5 is highly correlated with feature7High correlation
feature6 is highly correlated with feature0 and 8 other fieldsHigh correlation
feature7 is highly correlated with feature5 and 1 other fieldsHigh correlation
feature8 is highly correlated with feature2 and 4 other fieldsHigh correlation
feature9 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature10 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature11 is highly correlated with feature2 and 7 other fieldsHigh correlation
feature12 is highly correlated with feature0 and 9 other fieldsHigh correlation
feature13 is highly correlated with feature2 and 6 other fieldsHigh correlation
feature14 is highly correlated with feature2 and 7 other fieldsHigh correlation
feature15 is highly correlated with feature0 and 11 other fieldsHigh correlation
feature0 is highly correlated with feature1 and 5 other fieldsHigh correlation
feature1 is highly correlated with feature0 and 3 other fieldsHigh correlation
feature2 is highly correlated with feature0 and 4 other fieldsHigh correlation
feature4 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature6 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature8 is highly correlated with feature13High correlation
feature9 is highly correlated with feature0 and 3 other fieldsHigh correlation
feature10 is highly correlated with feature2High correlation
feature12 is highly correlated with feature14High correlation
feature13 is highly correlated with feature8High correlation
feature14 is highly correlated with feature12High correlation
feature15 is highly correlated with feature0 and 3 other fieldsHigh correlation
feature0 is highly correlated with feature1 and 5 other fieldsHigh correlation
feature1 is highly correlated with feature0 and 3 other fieldsHigh correlation
feature2 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature4 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature6 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature8 is highly correlated with feature2 and 4 other fieldsHigh correlation
feature9 is highly correlated with feature0 and 3 other fieldsHigh correlation
feature11 is highly correlated with feature2 and 5 other fieldsHigh correlation
feature12 is highly correlated with feature11 and 1 other fieldsHigh correlation
feature13 is highly correlated with feature2 and 4 other fieldsHigh correlation
feature14 is highly correlated with feature2 and 5 other fieldsHigh correlation
feature15 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature0 is highly correlated with feature1 and 7 other fieldsHigh correlation
feature1 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature2 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature3 is highly correlated with feature0 and 1 other fieldsHigh correlation
feature4 is highly correlated with feature0 and 6 other fieldsHigh correlation
feature6 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature8 is highly correlated with feature13 and 1 other fieldsHigh correlation
feature9 is highly correlated with feature0 and 7 other fieldsHigh correlation
feature10 is highly correlated with feature0 and 4 other fieldsHigh correlation
feature11 is highly correlated with feature2High correlation
feature12 is highly correlated with feature9 and 1 other fieldsHigh correlation
feature13 is highly correlated with feature2 and 1 other fieldsHigh correlation
feature14 is highly correlated with feature1 and 2 other fieldsHigh correlation
feature15 is highly correlated with feature0 and 5 other fieldsHigh correlation
feature7 is highly skewed (γ1 = 20.90353695) Skewed
feature11 is highly skewed (γ1 = 20.34934052) Skewed
feature7 has unique values Unique
feature0 has 66 (14.2%) zeros Zeros
feature1 has 66 (14.2%) zeros Zeros
feature2 has 172 (36.9%) zeros Zeros
feature8 has 172 (36.9%) zeros Zeros
feature10 has 20 (4.3%) zeros Zeros
feature11 has 177 (38.0%) zeros Zeros
feature12 has 172 (36.9%) zeros Zeros
feature13 has 172 (36.9%) zeros Zeros
feature14 has 172 (36.9%) zeros Zeros
feature15 has 172 (36.9%) zeros Zeros

Reproduction

Analysis started2022-07-28 21:27:20.981667
Analysis finished2022-07-28 21:27:57.613371
Duration36.63 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

feature0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.7435622
Minimum0
Maximum15400
Zeros66
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:57.830643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median150
Q3500
95-th percentile1550
Maximum15400
Range15400
Interquartile range (IQR)450

Descriptive statistics

Standard deviation984.5930652
Coefficient of variation (CV)2.244119686
Kurtosis125.8104721
Mean438.7435622
Median Absolute Deviation (MAD)150
Skewness9.493694
Sum204454.5
Variance969423.504
MonotonicityNot monotonic
2022-07-28T18:27:57.962403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5096
20.6%
066
14.2%
10048
 
10.3%
15036
 
7.7%
20022
 
4.7%
35019
 
4.1%
50017
 
3.6%
45015
 
3.2%
30014
 
3.0%
40011
 
2.4%
Other values (50)122
26.2%
ValueCountFrequency (%)
066
14.2%
5096
20.6%
10048
10.3%
1401
 
0.2%
15036
 
7.7%
20022
 
4.7%
25011
 
2.4%
30014
 
3.0%
3201
 
0.2%
35019
 
4.1%
ValueCountFrequency (%)
154001
0.2%
82401
0.2%
66001
0.2%
36801
0.2%
32251
0.2%
29001
0.2%
24001
0.2%
22001
0.2%
21501
0.2%
21401
0.2%

feature1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.847639485
Minimum0
Maximum31
Zeros66
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:58.087613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile21.75
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.836678814
Coefficient of variation (CV)1.410310902
Kurtosis4.520241857
Mean4.847639485
Median Absolute Deviation (MAD)1
Skewness2.205723214
Sum2259
Variance46.74017721
MonotonicityNot monotonic
2022-07-28T18:27:58.195561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1137
29.4%
066
14.2%
265
13.9%
334
 
7.3%
429
 
6.2%
616
 
3.4%
515
 
3.2%
811
 
2.4%
1210
 
2.1%
109
 
1.9%
Other values (20)74
15.9%
ValueCountFrequency (%)
066
14.2%
1137
29.4%
265
13.9%
334
 
7.3%
429
 
6.2%
515
 
3.2%
616
 
3.4%
79
 
1.9%
811
 
2.4%
97
 
1.5%
ValueCountFrequency (%)
318
1.7%
302
 
0.4%
281
 
0.2%
272
 
0.4%
262
 
0.4%
252
 
0.4%
233
 
0.6%
224
0.9%
214
0.9%
201
 
0.2%

feature2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct291
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1244.322468
Minimum0
Maximum40291.24
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:58.324756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median169.83
Q31017.375
95-th percentile5671.1625
Maximum40291.24
Range40291.24
Interquartile range (IQR)1017.375

Descriptive statistics

Standard deviation3558.699033
Coefficient of variation (CV)2.859949189
Kurtosis64.38595831
Mean1244.322468
Median Absolute Deviation (MAD)169.83
Skewness6.999722065
Sum579854.27
Variance12664338.81
MonotonicityNot monotonic
2022-07-28T18:27:58.455943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
89.92
 
0.4%
61.892
 
0.4%
159.92
 
0.4%
139.92
 
0.4%
662.281
 
0.2%
16513.31
 
0.2%
1459.271
 
0.2%
2140.361
 
0.2%
1676.091
 
0.2%
Other values (281)281
60.3%
ValueCountFrequency (%)
0172
36.9%
25.421
 
0.2%
35.311
 
0.2%
47.31
 
0.2%
50.631
 
0.2%
55.991
 
0.2%
56.171
 
0.2%
57.271
 
0.2%
61.892
 
0.4%
62.191
 
0.2%
ValueCountFrequency (%)
40291.241
0.2%
39673.41
0.2%
22028.161
0.2%
19078.481
0.2%
16513.31
0.2%
14895.471
0.2%
12133.731
0.2%
11470.791
0.2%
10812.151
0.2%
10685.141
0.2%

feature3
Real number (ℝ)

HIGH CORRELATION

Distinct458
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.60087983
Minimum-645.87
Maximum1521.9
Zeros4
Zeros (%)0.9%
Negative144
Negative (%)30.9%
Memory size3.8 KiB
2022-07-28T18:27:58.594825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-645.87
5-th percentile-21.55
Q1-0.4
median18.36
Q344.63
95-th percentile268.1275
Maximum1521.9
Range2167.77
Interquartile range (IQR)45.03

Descriptive statistics

Standard deviation122.093515
Coefficient of variation (CV)2.737468754
Kurtosis51.33382869
Mean44.60087983
Median Absolute Deviation (MAD)19.95
Skewness4.690918698
Sum20784.01
Variance14906.82639
MonotonicityNot monotonic
2022-07-28T18:27:58.720064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
0.9%
46.42
 
0.4%
472
 
0.4%
-0.12
 
0.4%
-2.62
 
0.4%
-0.082
 
0.4%
39.11
 
0.2%
-0.641
 
0.2%
10.671
 
0.2%
28.981
 
0.2%
Other values (448)448
96.1%
ValueCountFrequency (%)
-645.871
0.2%
-329.61
0.2%
-290.121
0.2%
-156.331
0.2%
-121.71
0.2%
-116.381
0.2%
-84.451
0.2%
-75.481
0.2%
-74.961
0.2%
-53.021
0.2%
ValueCountFrequency (%)
1521.91
0.2%
723.991
0.2%
505.81
0.2%
499.61
0.2%
452.671
0.2%
451.351
0.2%
431.851
0.2%
424.51
0.2%
3841
0.2%
373.81
0.2%

feature4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct440
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-434.2998927
Minimum-15506.35
Maximum-0.26
Zeros0
Zeros (%)0.0%
Negative466
Negative (%)100.0%
Memory size3.8 KiB
2022-07-28T18:27:58.850424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-15506.35
5-th percentile-1561.1625
Q1-492.035
median-154.525
Q3-50.18
95-th percentile-4.0875
Maximum-0.26
Range15506.09
Interquartile range (IQR)441.855

Descriptive statistics

Standard deviation975.5551982
Coefficient of variation (CV)-2.246270871
Kurtosis132.7139729
Mean-434.2998927
Median Absolute Deviation (MAD)136.255
Skewness-9.749161819
Sum-202383.75
Variance951707.9447
MonotonicityNot monotonic
2022-07-28T18:27:58.982001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-50.18
 
1.7%
-50.253
 
0.6%
-1503
 
0.6%
-100.72
 
0.4%
-32
 
0.4%
-50.652
 
0.4%
-2.12
 
0.4%
-12.32
 
0.4%
-102.12
 
0.4%
-3.62
 
0.4%
Other values (430)438
94.0%
ValueCountFrequency (%)
-15506.351
0.2%
-7966.541
0.2%
-6533.761
0.2%
-3186.791
0.2%
-2647.91
0.2%
-24441
0.2%
-24211
0.2%
-2232.11
0.2%
-2180.091
0.2%
-2165.851
0.2%
ValueCountFrequency (%)
-0.261
0.2%
-0.41
0.2%
-0.451
0.2%
-0.51
0.2%
-0.61
0.2%
-1.051
0.2%
-1.21
0.2%
-1.41
0.2%
-1.51
0.2%
-1.651
0.2%

feature5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct343
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4739911841
Minimum0.15
Maximum3.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:59.119373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.15
Q10.1736685394
median0.3038542263
Q30.56984818
95-th percentile1.416983948
Maximum3.15
Range3
Interquartile range (IQR)0.3961796406

Descriptive statistics

Standard deviation0.4522607499
Coefficient of variation (CV)0.9541543495
Kurtosis8.032681591
Mean0.4739911841
Median Absolute Deviation (MAD)0.1538542263
Skewness2.582784874
Sum220.8798918
Variance0.2045397859
MonotonicityNot monotonic
2022-07-28T18:27:59.251647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1553
 
11.4%
0.329
 
6.2%
0.1524
 
5.2%
0.55
 
1.1%
0.25
 
1.1%
0.25
 
1.1%
0.34
 
0.9%
12
 
0.4%
0.62
 
0.4%
0.352
 
0.4%
Other values (333)335
71.9%
ValueCountFrequency (%)
0.1524
5.2%
0.1553
11.4%
0.15003123051
 
0.2%
0.15014729951
 
0.2%
0.1503632591
 
0.2%
0.15055214721
 
0.2%
0.15064935061
 
0.2%
0.15090909091
 
0.2%
0.15091556461
 
0.2%
0.15096774191
 
0.2%
ValueCountFrequency (%)
3.151
0.2%
31
0.2%
2.4309523811
0.2%
2.3532975461
0.2%
2.2760899651
0.2%
2.2596385541
0.2%
2.2369565221
0.2%
2.1406251
0.2%
21
0.2%
1.9615384621
0.2%

feature6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct387
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean979.0708155
Minimum1
Maximum11731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:59.384879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.25
Q1133.5
median420
Q31238.75
95-th percentile3275.5
Maximum11731
Range11730
Interquartile range (IQR)1105.25

Descriptive statistics

Standard deviation1460.738442
Coefficient of variation (CV)1.491964033
Kurtosis18.21535502
Mean979.0708155
Median Absolute Deviation (MAD)351.5
Skewness3.601131187
Sum456247
Variance2133756.797
MonotonicityNot monotonic
2022-07-28T18:27:59.516894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3344
 
0.9%
244
 
0.9%
693
 
0.6%
1083
 
0.6%
553
 
0.6%
4173
 
0.6%
113
 
0.6%
1683
 
0.6%
1533
 
0.6%
1403
 
0.6%
Other values (377)434
93.1%
ValueCountFrequency (%)
12
0.4%
21
 
0.2%
31
 
0.2%
42
0.4%
72
0.4%
102
0.4%
113
0.6%
121
 
0.2%
141
 
0.2%
151
 
0.2%
ValueCountFrequency (%)
117311
0.2%
109971
0.2%
101671
0.2%
99841
0.2%
79061
0.2%
64571
0.2%
62491
0.2%
59861
0.2%
57751
0.2%
53861
0.2%

feature7
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
UNIQUE

Distinct466
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138846287
Minimum0.0006628654889
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:27:59.653978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006628654889
5-th percentile0.001927041969
Q10.003317865391
median0.005127240635
Q30.009698961926
95-th percentile0.04010591578
Maximum40
Range39.99933713
Interquartile range (IQR)0.006381096535

Descriptive statistics

Standard deviation1.87374643
Coefficient of variation (CV)16.45302313
Kurtosis444.5047377
Mean0.1138846287
Median Absolute Deviation (MAD)0.002273068028
Skewness20.90353695
Sum53.07023697
Variance3.510925686
MonotonicityNot monotonic
2022-07-28T18:27:59.892162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004548008081
 
0.2%
0.012708824431
 
0.2%
0.33816651081
 
0.2%
0.0016698550631
 
0.2%
0.0043868200431
 
0.2%
0.010123184631
 
0.2%
0.0026994141361
 
0.2%
0.0040196059981
 
0.2%
0.014103105851
 
0.2%
0.0061383543691
 
0.2%
Other values (456)456
97.9%
ValueCountFrequency (%)
0.00066286548891
0.2%
0.00089819431971
0.2%
0.0009804236191
0.2%
0.0010224439511
0.2%
0.0010945996051
0.2%
0.0011730381241
0.2%
0.0012704253731
0.2%
0.0012771994411
0.2%
0.001368002361
0.2%
0.0013689253941
0.2%
ValueCountFrequency (%)
401
0.2%
6.1111111111
0.2%
0.71712158811
0.2%
0.46666666671
0.2%
0.33816651081
0.2%
0.30905077261
0.2%
0.25177935941
0.2%
0.24259259261
0.2%
0.21135531141
0.2%
0.16666666671
0.2%

feature8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.857010006
Minimum0
Maximum281.6666667
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:00.028946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.937082378
Q32.815824155
95-th percentile12.79139376
Maximum281.6666667
Range281.6666667
Interquartile range (IQR)2.815824155

Descriptive statistics

Standard deviation15.60913163
Coefficient of variation (CV)4.046951293
Kurtosis221.631087
Mean3.857010006
Median Absolute Deviation (MAD)0.937082378
Skewness13.371251
Sum1797.366663
Variance243.6449903
MonotonicityNot monotonic
2022-07-28T18:28:00.153260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
1.544523811
 
0.2%
8.3215581541
 
0.2%
19.328079471
 
0.2%
1.7324537621
 
0.2%
3.0759588921
 
0.2%
1.4390767581
 
0.2%
0.9856977881
 
0.2%
1.7857855751
 
0.2%
9.8326255481
 
0.2%
Other values (285)285
61.2%
ValueCountFrequency (%)
0172
36.9%
0.1135125481
 
0.2%
0.1298310011
 
0.2%
0.14147150111
 
0.2%
0.15262235411
 
0.2%
0.17802335931
 
0.2%
0.19205689281
 
0.2%
0.20030060121
 
0.2%
0.20332676731
 
0.2%
0.2119157341
 
0.2%
ValueCountFrequency (%)
281.66666671
0.2%
98.886551391
0.2%
88.894339621
0.2%
62.167479671
0.2%
59.818257261
0.2%
52.168300651
0.2%
44.416666671
0.2%
32.133034381
0.2%
26.595679011
0.2%
25.627160491
0.2%

feature9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct465
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216437.824
Minimum1
Maximum3366472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:00.291616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1052.5
Q121131
median85328.5
Q3264503.5
95-th percentile857257.75
Maximum3366472
Range3366471
Interquartile range (IQR)243372.5

Descriptive statistics

Standard deviation350862.169
Coefficient of variation (CV)1.621076032
Kurtosis20.73834434
Mean216437.824
Median Absolute Deviation (MAD)80030
Skewness3.757792355
Sum100860026
Variance1.231042616 × 1011
MonotonicityNot monotonic
2022-07-28T18:28:00.424426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5402
 
0.4%
1673261
 
0.2%
626751
 
0.2%
21381
 
0.2%
616821
 
0.2%
307741
 
0.2%
2168291
 
0.2%
3196991
 
0.2%
3719271
 
0.2%
189321
 
0.2%
Other values (455)455
97.6%
ValueCountFrequency (%)
11
0.2%
91
0.2%
461
0.2%
791
0.2%
1651
0.2%
1681
0.2%
1851
0.2%
2201
0.2%
3701
0.2%
3791
0.2%
ValueCountFrequency (%)
33664721
0.2%
23124221
0.2%
21087061
0.2%
17436421
0.2%
17227541
0.2%
16460561
0.2%
15268911
0.2%
15193581
0.2%
15046561
0.2%
13917381
0.2%

feature10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct446
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6930.456438
Minimum0
Maximum237182.78
Zeros20
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:00.563683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.715
Q1383.6875
median1410.855
Q35212.9775
95-th percentile37783.3075
Maximum237182.78
Range237182.78
Interquartile range (IQR)4829.29

Descriptive statistics

Standard deviation17581.80082
Coefficient of variation (CV)2.536889305
Kurtosis71.09879174
Mean6930.456438
Median Absolute Deviation (MAD)1273.435
Skewness6.910017543
Sum3229592.7
Variance309119720
MonotonicityNot monotonic
2022-07-28T18:28:00.692234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020
 
4.3%
159.92
 
0.4%
33441.061
 
0.2%
1661.331
 
0.2%
41366.031
 
0.2%
128238.121
 
0.2%
1338.891
 
0.2%
615.131
 
0.2%
348.31
 
0.2%
225.961
 
0.2%
Other values (436)436
93.6%
ValueCountFrequency (%)
020
4.3%
38.281
 
0.2%
44.481
 
0.2%
48.891
 
0.2%
49.991
 
0.2%
56.891
 
0.2%
56.91
 
0.2%
61.881
 
0.2%
67.991
 
0.2%
76.991
 
0.2%
ValueCountFrequency (%)
237182.781
0.2%
128238.121
0.2%
984541
0.2%
84501.861
0.2%
81751.51
0.2%
75394.591
0.2%
67852.961
0.2%
62793.921
0.2%
59018.811
0.2%
52806.341
0.2%

feature11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct289
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4373333912
Minimum0
Maximum73.08063374
Zeros177
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:00.846998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.082514574
Q30.3113112272
95-th percentile1.028115782
Maximum73.08063374
Range73.08063374
Interquartile range (IQR)0.3113112272

Descriptive statistics

Standard deviation3.442093848
Coefficient of variation (CV)7.870640379
Kurtosis429.2248992
Mean0.4373333912
Median Absolute Deviation (MAD)0.082514574
Skewness20.34934052
Sum203.7973603
Variance11.84801006
MonotonicityNot monotonic
2022-07-28T18:28:00.974946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0177
38.0%
12
 
0.4%
0.0057869119131
 
0.2%
0.30128501471
 
0.2%
0.40570999251
 
0.2%
0.4325389921
 
0.2%
0.67982494191
 
0.2%
0.083846271761
 
0.2%
0.39777871971
 
0.2%
0.6057256191
 
0.2%
Other values (279)279
59.9%
ValueCountFrequency (%)
0177
38.0%
0.0044366176291
 
0.2%
0.00572304731
 
0.2%
0.0057869119131
 
0.2%
0.0066894789341
 
0.2%
0.0068144175181
 
0.2%
0.0081562093281
 
0.2%
0.011712415841
 
0.2%
0.013226737861
 
0.2%
0.016578231451
 
0.2%
ValueCountFrequency (%)
73.080633741
0.2%
7.4366451611
0.2%
7.3847470821
0.2%
5.0664195551
0.2%
3.8498094781
0.2%
3.7867244831
0.2%
2.8939883651
0.2%
2.5757057931
0.2%
2.4131558741
0.2%
2.3555007141
0.2%

feature12
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.88619003
Minimum0
Maximum2232.1
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:01.112188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.69256757
Q365.49809783
95-th percentile282.20875
Maximum2232.1
Range2232.1
Interquartile range (IQR)65.49809783

Descriptive statistics

Standard deviation142.5215232
Coefficient of variation (CV)2.302961664
Kurtosis117.4831179
Mean61.88619003
Median Absolute Deviation (MAD)19.69256757
Skewness8.667886527
Sum28838.96455
Variance20312.38456
MonotonicityNot monotonic
2022-07-28T18:28:01.238624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
421
 
0.2%
21.11063831
 
0.2%
6.8636363641
 
0.2%
95.034615381
 
0.2%
27.2451
 
0.2%
93.151
 
0.2%
56.2851
 
0.2%
47.59251
 
0.2%
6.7431818181
 
0.2%
Other values (285)285
61.2%
ValueCountFrequency (%)
0172
36.9%
1.3251
 
0.2%
1.4661971831
 
0.2%
1.651
 
0.2%
1.71
 
0.2%
2.1777108431
 
0.2%
2.78751
 
0.2%
31
 
0.2%
3.241
 
0.2%
3.281
 
0.2%
ValueCountFrequency (%)
2232.11
0.2%
653.851
0.2%
641.451
0.2%
598.81
0.2%
556.2751
0.2%
5161
0.2%
473.41
0.2%
4591
0.2%
456.91
0.2%
430.221
0.2%

feature13
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct286
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00863418767
Minimum0
Maximum0.204610951
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:01.368754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.003207436255
Q30.009515425889
95-th percentile0.03504585607
Maximum0.204610951
Range0.204610951
Interquartile range (IQR)0.009515425889

Descriptive statistics

Standard deviation0.01786624736
Coefficient of variation (CV)2.069244734
Kurtosis41.59893819
Mean0.00863418767
Median Absolute Deviation (MAD)0.003207436255
Skewness5.414284197
Sum4.023531454
Variance0.0003192027949
MonotonicityNot monotonic
2022-07-28T18:28:01.493372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
0.011494252873
 
0.6%
0.0070921985823
 
0.6%
0.0084745762712
 
0.4%
0.015151515152
 
0.4%
0.0071942446042
 
0.4%
0.017094017092
 
0.4%
0.0032362459552
 
0.4%
0.0060268891981
 
0.2%
0.013201320131
 
0.2%
Other values (276)276
59.2%
ValueCountFrequency (%)
0172
36.9%
0.00035137034431
 
0.2%
0.00044984255511
 
0.2%
0.00045516613561
 
0.2%
0.00059417706481
 
0.2%
0.0006265664161
 
0.2%
0.00076423385561
 
0.2%
0.00077519379841
 
0.2%
0.00080450522931
 
0.2%
0.00080775444261
 
0.2%
ValueCountFrequency (%)
0.2046109511
0.2%
0.11764705881
0.2%
0.11479944671
0.2%
0.10714285711
0.2%
0.11
0.2%
0.095238095241
0.2%
0.090909090911
0.2%
0.088235294121
0.2%
0.063063063061
0.2%
0.053811659191
0.2%

feature14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct290
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.71335989
Minimum0
Maximum2154
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:01.623038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median72.52428571
Q3127.3015046
95-th percentile231.60225
Maximum2154
Range2154
Interquartile range (IQR)127.3015046

Descriptive statistics

Standard deviation145.4264371
Coefficient of variation (CV)1.657973624
Kurtosis96.5757394
Mean87.71335989
Median Absolute Deviation (MAD)72.52428571
Skewness7.948637197
Sum40874.42571
Variance21148.84862
MonotonicityNot monotonic
2022-07-28T18:28:01.748408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
89.93
 
0.6%
61.892
 
0.4%
159.92
 
0.4%
139.92
 
0.4%
94.611428571
 
0.2%
132.66090911
 
0.2%
164.64307691
 
0.2%
83.80451
 
0.2%
134.051
 
0.2%
Other values (280)280
60.1%
ValueCountFrequency (%)
0172
36.9%
25.24751
 
0.2%
25.421
 
0.2%
33.991
 
0.2%
35.311
 
0.2%
40.111
 
0.2%
43.8851
 
0.2%
47.31
 
0.2%
47.991
 
0.2%
49.898333331
 
0.2%
ValueCountFrequency (%)
21541
0.2%
1175.021
0.2%
8451
0.2%
791.51428571
0.2%
734.281
0.2%
411.37434781
0.2%
393.5251
0.2%
372.61
0.2%
360.22636361
0.2%
350.35751
0.2%

feature15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct56
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.31330472
Minimum0
Maximum541
Zeros172
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2022-07-28T18:28:01.992299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile42.5
Maximum541
Range541
Interquartile range (IQR)8

Descriptive statistics

Standard deviation33.6252036
Coefficient of variation (CV)3.260371385
Kurtosis143.3605913
Mean10.31330472
Median Absolute Deviation (MAD)2
Skewness10.31573678
Sum4806
Variance1130.654317
MonotonicityNot monotonic
2022-07-28T18:28:02.124936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
36.9%
160
 
12.9%
241
 
8.8%
424
 
5.2%
320
 
4.3%
712
 
2.6%
611
 
2.4%
810
 
2.1%
207
 
1.5%
57
 
1.5%
Other values (46)102
21.9%
ValueCountFrequency (%)
0172
36.9%
160
 
12.9%
241
 
8.8%
320
 
4.3%
424
 
5.2%
57
 
1.5%
611
 
2.4%
712
 
2.6%
810
 
2.1%
96
 
1.3%
ValueCountFrequency (%)
5411
0.2%
2781
0.2%
1531
0.2%
1441
0.2%
1361
0.2%
1181
0.2%
1051
0.2%
941
0.2%
921
0.2%
841
0.2%

target
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
1
260 
0
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters466
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Length

2022-07-28T18:28:02.239152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-28T18:28:02.332985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Most occurring characters

ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number466
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1260
55.8%
0206
44.2%

Interactions

2022-07-28T18:27:55.545642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.257507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.190034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.902208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.783967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.738171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.842834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.660752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.917330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.421844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:43.303240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.102092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.883524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.806287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.708853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.570275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.651611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.430022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.311832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.020329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.887684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.853014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.958205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.779207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.105947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.636978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:43.494084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.213529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.001627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.912860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.905424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.677767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.753319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.630590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.459416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.129097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.999636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.963955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.059613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.907474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.236820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.811717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:43.624514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.315679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.109090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.016943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.011140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.778114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.861921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.745337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.649464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.279134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.188169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.082479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.174984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.017249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.427098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.995281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:43.831500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.463781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.225474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.144091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.133048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.885060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.956901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.848945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.819286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.455026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.435186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.184406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.274815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.115907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.602229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.091859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.016371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.593459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.326865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.246505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.253723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.011503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.064452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:23.971628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.996677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.573079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.618224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.315331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.412887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.244869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.791242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.209523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.152270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.732519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.550537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.356991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.364968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.136762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.166831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.084233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:26.235810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.681215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.729578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.460828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.542747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.384554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:38.974234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.385538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.334242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.866648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.730059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.458349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.476867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.290556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.273846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.196013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:26.413244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.789132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.829901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.611129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.679130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.522492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:39.162118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.561818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.515366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:46.972816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.841014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.562889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.580107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.401006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.380821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.317362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:26.597559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:28.911887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:30.950629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.759132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.830534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.758600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:39.349812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.746544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.704430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.079369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:48.957247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.669058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.711860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.521559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.482305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.427016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:26.783867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.016807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.049250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:32.884430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:34.940682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:36.947577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:39.527440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:41.930115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:44.883827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.188186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.059535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.770113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.816363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.651917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.587453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.544498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.031351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.135236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.161985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.013923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.049562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.059389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:39.714305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:42.176674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.071449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.299290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.175060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.876639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:52.938471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.877506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.681279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.647562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.306613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.247282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.260083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.131488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.155312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.175593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:39.888896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:42.407669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.357207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.393947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.282648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:50.975311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.031239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:54.975729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.786539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.763956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.409579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.364655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.361974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.274085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.264431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.367431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.002040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:42.594985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.544925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.496758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.393186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.078190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.133889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.087301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.878673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.868844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.505501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.467691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.453587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.383523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.362123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.489276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.107349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:42.775512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.727632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.595139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.494309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.175427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.232999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.188886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:56.976977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:24.977595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.655829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.573016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.546582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.622392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.461517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.616910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.211778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:42.952965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.890149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.691195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.599074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.273851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.350169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.287839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:57.073151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:25.082120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:27.802189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:29.677806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:31.640152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:33.735054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:35.559286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:37.747981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:40.313924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:43.129209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:45.992266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:47.787977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:49.700170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:51.523835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:53.462047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-28T18:27:55.380299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-28T18:28:02.437938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-28T18:28:02.626795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-28T18:28:02.816673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-28T18:28:03.009629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-28T18:27:57.254582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-28T18:27:57.514864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

feature0feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13feature14feature15target
0200.02662.2839.10-188.550.2469787610.0045483.52370316732633441.060.01980426.8500000.00919894.61142970
1150.020.00149.55-0.450.15000030.0379750.0000007978.930.0000000.0000000.0000000.00000001
250.01346.0830.41-102.102.430952420.0042393.389618990718858.770.01835125.5250000.09523886.52000040
3100.020.0043.84-56.160.1509683720.0058540.000000635441164.110.0000000.0000000.0000000.00000001
450.0187.56-3.05-94.500.4126642290.0045720.926561500891786.260.04901994.5000000.00436787.56000010
5850.0111435.681.60-848.400.58916714400.0038791.6922213711852745.780.52286840.4000000.01458368.365714211
650.01436.856.80-82.150.2381163450.0183755.31771218776959.570.45525620.5375000.011594109.21250041
70.000.00-6.50-16.800.1500001120.0172310.00000065006692.130.0000000.0000000.0000000.00000000
8100.011027.8679.76-110.560.3625002880.0150989.845402190753611.200.2846318.0307690.04513979.066154130
9600.0122876.013.68-596.320.26525122470.0045844.8253584901912324.271.23738231.3694740.008456151.368947191

Last rows

feature0feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13feature14feature15target
45650.010.00-0.30-69.000.1500004250.0111820.00000038007342.780.0000000.0000000.0000000.00000001
4571600.043346.25106.90-1886.300.54973932590.0036741.8677448871255209.100.64238589.5800000.006137167.312500200
458100.02292.0718.40-104.350.5507981880.0097072.820570193682335.520.12505634.5166670.01595797.35666731
4590.000.00-0.10-50.100.1500003340.0112950.00000029570189.980.0000000.0000000.0000000.00000001
4601050.0211955.8317.20-1231.200.60000019680.0077181.6563602550023013.010.64912873.8000000.008130122.239375161
4611300.0712133.73244.30-1081.900.39530726420.00815211.61789532408117343.280.69962112.8938270.030659149.799136810
462350.010.00-1.99-351.990.6848055140.0040000.0000001284992263.650.0000000.0000000.0000000.00000001
463400.041100.07-18.20-438.600.7484645860.0036832.5081401591095303.100.20743962.6571430.011945157.15285770
464150.010.00114.30-35.700.3000001190.0038340.00000031040334.590.0000000.0000000.0000000.00000000
46550.010.00-1.00-51.001.961538260.0025370.000000102500.000.0000000.0000000.0000000.00000001